The Bag-of-Words (BoW) representation is well applied to recent
state-of-the-art image retrieval works. Typically, multiple vocabularies are
generated to correct quantization artifacts and improve recall. However, this
routine is corrupted by vocabulary correlation, i.e., overlapping among
different vocabularies. Vocabulary correlation leads to an over-counting of the
indexed features in the overlapped area, or the intersection set, thus
compromising the retrieval accuracy. In order to address the correlation
problem while preserve the benefit of high recall, this paper proposes a Bayes
merging approach to down-weight the indexed features in the intersection set.
Through explicitly modeling the correlation problem in a probabilistic view, a
joint similarity on both image- and feature-level is estimated for the indexed
features in the intersection set.
We evaluate our method through extensive experiments on three benchmark
datasets. Albeit simple, Bayes merging can be well applied in various merging
tasks, and consistently improves the baselines on multi-vocabulary merging.
Moreover, Bayes merging is efficient in terms of both time and memory cost, and
yields competitive performance compared with the state-of-the-art methods.

本文提出了一种贝叶斯合并方法来解决多词汇表中的词汇相关问题，并通过基于图像和特征级别的联合相似度评估其有效性。经过广泛的实验，该方法证明了在多词汇表合并中的竞争性性能。

多词汇的贝叶斯融合 -- 可伸缩图像检索

Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval

In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low
discriminative power, so false positive matches occur prevalently. Apart from
the information loss during quantization, another cause is that the SIFT
feature only describes the local gradient distribution. To address this
problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform
feature fusion at indexing level. Basically, complementary features are coupled
into a multi-dimensional inverted index. Each dimension of c-MI corresponds to
one kind of feature, and the retrieval process votes for images similar in both
SIFT and other feature spaces. Specifically, we exploit the fusion of local
color feature into c-MI. While the precision of visual match is greatly
enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation
of SIFT and color features significantly reduces the impact of false positive
matches.
Extensive experiments on several benchmark datasets demonstrate that c-MI
improves the retrieval accuracy significantly, while consuming only half of the
query time compared to the baseline. Importantly, we show that c-MI is well
complementary to many prior techniques. Assembling these methods, we have
obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench
datasets, respectively, which compare favorably with the state-of-the-arts.

该论文提出了一种耦合多索引 (c-MI) 框架，借助局部颜色特征融合 SIFT 特征，增强了图像检索的准确性和召回率。实验证明 c-MI 基于 Bag-of-Words 的图像检索方法，可显著提高检索精度，且比基线方法运行更快且兼容先前技术。